The Commercial Determinants of Men’s Health Promotion: A Case Study of Gambling, Nonnies, and Athleisurewear
Bibliographic record
Abstract
Although the social determinants of health have guided equity work with the tailoring of men's health promotion programs, the role of, and potential for, the commercial determinants of health in those interventions is rarely addressed and poorly understood. While four commercial products, tobacco, alcohol, ultra-processed food, and fossil fuels, account for more than a third of global deaths, there is a need to recognize that consumer goods industries can make both positive and negative contributions to health. This article begins much-needed discussions about what we might learn from, and strategically tap in the commercial sector to seed, scale, and sustain men's health promotion programs. Three case studies, online sports betting, beer and the rise of the nonny, and athleisurewear, are discussed. Connections between online sports betting and masculinities explain young men's disproportionate involvement and gambling addictions with recommendations to legislate an end to gambling advertisements and de-incentivize industry profiteering through penalties and higher taxes. Regarding beer and the rise of the nonny, brewers have innovated with non-alcoholic beer based on shifting consumption patterns and masculinities in their core market-men. The nonny reminds health promoters to know their end-user's values and behaviors to bolster program acceptability. Detailing Under Armour and Lululemon, two highly gendered but diversifying athleisurewear brands, the complexities of, and potential for, leveraging public health and industry collaborations are underscored. Taken together, the article findings suggest men's health promoters should rigorously explore tapping key commercial entities and tax revenues to advance the health of men and their communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".